CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python, Matlab/Octave, Julia or Javascript
YOLO11 research prototype for detecting people associated with potentially dangerous objects in public-area imagery, with thesis evidence, reproducibility notes, and responsible-use guidance.